// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <include/ocr_det.h>

namespace PaddleOCR {

void DBDetector::LoadModel(const std::string& model_dir)
{
    //   AnalysisConfig config;
    paddle_infer::Config config;
    config.SetModel(
        model_dir + "/inference.pdmodel", model_dir + "/inference.pdiparams"
    );

    if (this->use_gpu_) {
        config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
        if (this->use_tensorrt_) {
            auto precision = paddle_infer::Config::Precision::kFloat32;
            if (this->precision_ == "fp16") {
                precision = paddle_infer::Config::Precision::kHalf;
            }
            if (this->precision_ == "int8") {
                precision = paddle_infer::Config::Precision::kInt8;
            }
            config.EnableTensorRtEngine(
                1 << 20, 10, 3, precision, false, false
            );
            std::map<std::string, std::vector<int>> min_input_shape = {
                {"x",                         {1, 3, 50, 50} },
                {"conv2d_92.tmp_0",           {1, 96, 20, 20}},
                {"conv2d_91.tmp_0",           {1, 96, 10, 10}},
                {"nearest_interp_v2_1.tmp_0", {1, 96, 10, 10}},
                {"nearest_interp_v2_2.tmp_0", {1, 96, 20, 20}},
                {"nearest_interp_v2_3.tmp_0", {1, 24, 20, 20}},
                {"nearest_interp_v2_4.tmp_0", {1, 24, 20, 20}},
                {"nearest_interp_v2_5.tmp_0", {1, 24, 20, 20}},
                {"elementwise_add_7",         {1, 56, 2, 2}  },
                {"nearest_interp_v2_0.tmp_0", {1, 96, 2, 2}  }
            };
            std::map<std::string, std::vector<int>> max_input_shape = {
                {"x",                         {1, 3, this->max_side_len_, this->max_side_len_}},
                {"conv2d_92.tmp_0",           {1, 96, 400, 400}                               },
                {"conv2d_91.tmp_0",           {1, 96, 200, 200}                               },
                {"nearest_interp_v2_1.tmp_0", {1, 96, 200, 200}                               },
                {"nearest_interp_v2_2.tmp_0", {1, 96, 400, 400}                               },
                {"nearest_interp_v2_3.tmp_0", {1, 24, 400, 400}                               },
                {"nearest_interp_v2_4.tmp_0", {1, 24, 400, 400}                               },
                {"nearest_interp_v2_5.tmp_0", {1, 24, 400, 400}                               },
                {"elementwise_add_7",         {1, 56, 400, 400}                               },
                {"nearest_interp_v2_0.tmp_0", {1, 96, 400, 400}                               }
            };
            std::map<std::string, std::vector<int>> opt_input_shape = {
                {"x",                         {1, 3, 640, 640} },
                {"conv2d_92.tmp_0",           {1, 96, 160, 160}},
                {"conv2d_91.tmp_0",           {1, 96, 80, 80}  },
                {"nearest_interp_v2_1.tmp_0", {1, 96, 80, 80}  },
                {"nearest_interp_v2_2.tmp_0", {1, 96, 160, 160}},
                {"nearest_interp_v2_3.tmp_0", {1, 24, 160, 160}},
                {"nearest_interp_v2_4.tmp_0", {1, 24, 160, 160}},
                {"nearest_interp_v2_5.tmp_0", {1, 24, 160, 160}},
                {"elementwise_add_7",         {1, 56, 40, 40}  },
                {"nearest_interp_v2_0.tmp_0", {1, 96, 40, 40}  }
            };

            config.SetTRTDynamicShapeInfo(
                min_input_shape, max_input_shape, opt_input_shape
            );
        }
    } else {
        config.DisableGpu();
        if (this->use_mkldnn_) {
            config.EnableMKLDNN();
            // cache 10 different shapes for mkldnn to avoid memory leak
            config.SetMkldnnCacheCapacity(10);
        }
        config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
    }
    // use zero_copy_run as default
    config.SwitchUseFeedFetchOps(false);
    // true for multiple input
    config.SwitchSpecifyInputNames(true);

    config.SwitchIrOptim(true);

    config.EnableMemoryOptim();
    // config.DisableGlogInfo();

    this->predictor_ = CreatePredictor(config);
}

void DBDetector::Run(
    cv::Mat& img,
    std::vector<std::vector<std::vector<int>>>& boxes,
    std::vector<double>& times
)
{
    float ratio_h{};
    float ratio_w{};

    cv::Mat srcimg;
    cv::Mat resize_img;
    img.copyTo(srcimg);

    auto preprocess_start = std::chrono::steady_clock::now();
    this->resize_op_.Run(
        img, resize_img, this->max_side_len_, ratio_h, ratio_w,
        this->use_tensorrt_
    );

    this->normalize_op_.Run(
        &resize_img, this->mean_, this->scale_, this->is_scale_
    );

    std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
    this->permute_op_.Run(&resize_img, input.data());
    auto preprocess_end = std::chrono::steady_clock::now();

    // Inference.
    auto input_names = this->predictor_->GetInputNames();
    auto input_t = this->predictor_->GetInputHandle(input_names[0]);
    input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
    auto inference_start = std::chrono::steady_clock::now();
    input_t->CopyFromCpu(input.data());

    this->predictor_->Run();

    std::vector<float> out_data;
    auto output_names = this->predictor_->GetOutputNames();
    auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
    std::vector<int> output_shape = output_t->shape();
    int out_num = std::accumulate(
        output_shape.begin(), output_shape.end(), 1, std::multiplies<int>()
    );

    out_data.resize(out_num);
    output_t->CopyToCpu(out_data.data());
    auto inference_end = std::chrono::steady_clock::now();

    auto postprocess_start = std::chrono::steady_clock::now();
    int n2 = output_shape[2];
    int n3 = output_shape[3];
    int n = n2 * n3;

    std::vector<float> pred(n, 0.0);
    std::vector<unsigned char> cbuf(n, ' ');

    for (int i = 0; i < n; i++) {
        pred[i] = float(out_data[i]);
        cbuf[i] = (unsigned char)((out_data[i]) * 255);
    }

    cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char*)cbuf.data());
    cv::Mat pred_map(n2, n3, CV_32F, (float*)pred.data());

    const double threshold = this->det_db_thresh_ * 255;
    const double maxvalue = 255;
    cv::Mat bit_map;
    cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
    if (this->use_dilation_) {
        cv::Mat dila_ele =
            cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
        cv::dilate(bit_map, bit_map, dila_ele);
    }

    boxes = post_processor_.BoxesFromBitmap(
        pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
        this->det_db_score_mode_
    );

    boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
    auto postprocess_end = std::chrono::steady_clock::now();

    std::chrono::duration<float> preprocess_diff =
        preprocess_end - preprocess_start;
    times.push_back(double(preprocess_diff.count() * 1000));
    std::chrono::duration<float> inference_diff =
        inference_end - inference_start;
    times.push_back(double(inference_diff.count() * 1000));
    std::chrono::duration<float> postprocess_diff =
        postprocess_end - postprocess_start;
    times.push_back(double(postprocess_diff.count() * 1000));
}

} // namespace PaddleOCR
